Ocean adapta data

By species data exploration

  • for each region, year, species combination, the mean location and biomass, shows species averages.

By species data exploration

  • for every region and year, all of the species for that region, including zeros if they were not present.

O.K. so now we have a wtcpue column here. Lets now get some data wrangling going and exploration figures

Survey plots

plot_data <- dat_exploded %>% 
  group_by(spp,year,lat,lon,depth) %>%
  summarise_if(is_numeric,mean,na.rm=T) %>%
  mutate(
    year = as.numeric(year)
  )
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Histogram of data points

Timeline of depth and wtcpue

Boxplot of data

Survey maps

us_map <- st_as_sf(map("state", plot = FALSE, fill = TRUE))

path_world <- MyFunctions::my_path("G", extra_path = "Spatial/SAU/SAU_Shapefile")
path_world <- "~/Downloads/SAU_Shapefile"

# The File
fnam_world <- "SAUEEZ_July2015.shp"

# Load it!
World_eez_sf <- st_read(dsn = path_world,
                        layer =file_path_sans_ext(fnam_world)) %>% 
  rename(eez_name = Name) %>% 
  st_transform(crs = 4326) %>% # 4326
  # st_transform(crs = "+proj=eck4") %>% # for tbular plot
  st_simplify(preserveTopology = TRUE, dTolerance = 0.1) %>% 
  filter(eez_name == "USA (East Coast)")

# Set manual breaks for bins
bins <- seq(1970,2020,5)

map_data <- dat_exploded %>% 
  group_by(spp,year,lat,lon,depth) %>%
  summarise_if(is_numeric,mean,na.rm=T) %>%
  mutate(
    year = as.numeric(year),
    period = cut(year, breaks = bins)
  ) %>% 
  group_by(period,lat,lon) %>% 
  summarise_if(is.numeric,mean,na.rm=T) %>% 
  filter(wtcpue>0) 

Map of smapling points

In this case the color gradiant represents year from 1972 to 2019

Density map

This is a simple 2d kenell analysis using ggplot2::stat_density_2d() of the sampling points

Looks to me that the stock might not be shifting per se but expanding its northern range

CPUE (wt CPUE) data

ggplot(us_map) +
  geom_sf() +
  geom_sf(data = World_eez_sf, aes(), fill = "white") +
  geom_point(data = subset(map_data, wtcpue < 100),
             aes(
               x = lon,
               y = lat,
               color = wtcpue
             ),
             
  ) +
  # facet_wrap(~period,
  #            nrow = 2) +
  scale_color_distiller(palette = "Spectral", 
                       guide_legend(title = "CPUE")) + 
  # viridis::scale_fill_viridis() +
  # viridis::scale_colour_viridis() +
  coord_sf(xlim = c(-76, -65),ylim = c(35, 45)) +
  MyFunctions::my_ggtheme_m()
)
Error: unexpected ')' in ")"

ERDDAP

This is the data extracted directly from NOAA in has four available surveys (OceanAdapt only two)

---
title: "Ocean adapt exploration script"
output:
  html_document:
    df_print: paged
---

```{r setup, include=FALSE}
MyFunctions::my_lib(c("ggmap","sf","tidyverse","tools","readr","data.table","maps"))
```

# Ocean adapta data


```{r basic_exploratin, eval = F, echo = F}

head(by_species)
names(by_species)
unique(by_species$spp)
unique(by_species$region)
max(by_species$year)
min(by_species$year)

```


## By species data exploration

- for each region, year, species combination, the mean location and biomass, shows species averages.

```{r by_species_data, eval = T, echo = T}


by_species <- read_csv(here::here("data_clean/by_species.csv"))

head(by_species)

by_species %>% 
  filter(spp == "Centropristis striata",
         region %in% c("Northeast US Fall" , "Northeast US Spring")
         )


```



```{r plot_by_species, eval = T, echo = T, message = F, warning = F}

suppressWarnings(
  by_species %>% 
    filter(spp == "Centropristis striata",
           region %in% c("Northeast US Fall" , "Northeast US Spring")) %>% 
    group_by(spp,year,lat,lon,depth) %>%
    summarise_if(is_numeric,mean) %>%
    ggplot() +
    geom_point(
      aes(
        x = lon,
        y = lat,
        fill = as.numeric(year),
        col = as.numeric(year)
      )
    )
)
```


## By species data exploration

- for every region and year, all of the species for that region, including zeros if they were not present.

```{r dat_exploded, eval = T, echo = T}

dat_exploded <- readRDS("/Volumes/Enterprise/Data/pinskylab-OceanAdapt-966adf0/data_clean/dat_exploded.rds") %>% 
  filter(spp == "Centropristis striata",
         region %in% c("Northeast US Fall" , "Northeast US Spring")) #No more seasons

head(dat_exploded)



```

O.K. so now we have a `wtcpue` column here. Lets now get some data wrangling going and exploration figures

### Survey plots

```{r plot_data, eval = T, echo = T, fig.width = 12, fig.height = 8}

plot_data <- dat_exploded %>% 
  group_by(spp,year,lat,lon,depth) %>%
  summarise_if(is_numeric,mean,na.rm=T) %>%
  mutate(
    year = as.numeric(year)
  )

```

#### Histogram of data points

```{r histogram, eval = T, echo = T}

plot_data %>% 
  filter(wtcpue < 10,
         wtcpue >0) %>% #remove THAT one outlier
  mutate(depth = depth*-1) %>% 
  gather("variable","value",depth:wtcpue) %>% 
  # View(.)
    ggplot() +
    geom_histogram(
      aes(
        x = value,
      ),
      binwidth = 1
    ) +
  facet_wrap(~variable,
             ncol = 1,
             scales = "free_x")

```

#### Timeline of depth and wtcpue

```{r timeline, eval = T, echo = T}

plot_data %>% 
  # filter(wtcpue < 200) %>% #remove THAT one outlier
  mutate(depth = depth*-1) %>%
  gather("variable","value",depth:wtcpue) %>% 
  mutate(lat_round = round(lat)) %>% 
  group_by(variable,year,lat_round) %>% 
  summarise_at(vars(value),sum) %>% 
  # View(.)
    ggplot() +
    geom_line(
      aes(
        x = year,
        y = value,
        col = as.factor(lat_round)
      )
    ) +
  facet_wrap(~variable,
             ncol = 1,
             scales = "free_y") +
  MyFunctions::my_ggtheme_p()



```

#### Boxplot of data 

```{r boxplot, eval = T, echo = T}

plot_data %>% 
  # filter(wtcpue < 200) %>% #remove THAT one outlier
  mutate(depth = depth*-1) %>%
  gather("variable","value",depth:wtcpue) %>% 
  mutate(lat_round = round(lat)) %>% 
    ggplot() +
    geom_boxplot(
      aes(
        x = as.factor(lat_round),
        y = value
        # fill = as.factor(year)
      )
    ) +
  facet_wrap(~variable,
             # ncol = 2,
             scales = "free")



```



```{r time_line_facet, eval = T, echo = T}

# Set manual breaks for bins
bins <- seq(1970,2020,10)


plot_data %>% 
  # filter(wtcpue < 200) %>% #remove THAT one outlier
  mutate(depth = depth*-1) %>%
  gather("variable","value",depth:wtcpue) %>% 
  mutate(lat_round = round(lat),
         period = cut(year, breaks = bins)) %>% 
  group_by(variable,year,period,lat_round) %>% 
  summarise_at(vars(value),sum,na.rm=T) %>% 
  # View(.)
    ggplot() +
    geom_line(
      aes(
        x = year,
        y = value#,
        # col = ,
        # fill = as.factor(year)
      )
    ) +
  facet_wrap(~variable+lat_round,
             # ncol = 2,
             scales = "free_y")


gc()
```


### Survey maps

```{r maps_sf, eval = F, echo = T, results = 'hide'}

us_map <- st_as_sf(map("state", plot = FALSE, fill = TRUE))

path_world <- MyFunctions::my_path("G", extra_path = "Spatial/SAU/SAU_Shapefile")
path_world <- "~/Downloads/SAU_Shapefile"

# The File
fnam_world <- "SAUEEZ_July2015.shp"

# Load it!
World_eez_sf <- st_read(dsn = path_world,
                        layer =file_path_sans_ext(fnam_world)) %>% 
  rename(eez_name = Name) %>% 
  st_transform(crs = 4326) %>% # 4326
  # st_transform(crs = "+proj=eck4") %>% # for tbular plot
  st_simplify(preserveTopology = TRUE, dTolerance = 0.1) %>% 
  filter(eez_name == "USA (East Coast)")

```

```{r map_data, eval = T, echo = T, fig.width = 12, fig.height = 8}

# Set manual breaks for bins
bins <- seq(1970,2020,5)

map_data <- dat_exploded %>% 
  group_by(spp,year,lat,lon,depth) %>%
  summarise_if(is_numeric,mean,na.rm=T) %>%
  mutate(
    year = as.numeric(year),
    period = cut(year, breaks = bins)
  ) %>% 
  group_by(period,lat,lon) %>% 
  summarise_if(is.numeric,mean,na.rm=T) %>% 
  filter(wtcpue>0) 

```

#### Map of smapling points

In this case the color gradiant represents year from 1972 to 2019

```{r survey_map, eval = T, echo = T, fig.width = 12, fig.height = 8}

ggplot(us_map) +
  geom_sf() +
  geom_sf(data = World_eez_sf, aes(), fill = "white") +
  geom_point(data = map_data,
             aes(
               x = lon,
               y = lat,
               color = year
             ),
             size = 1
  ) +
  scale_color_distiller(palette = "Spectral", 
                       guide_legend(title = "Probability \ndensity")) + 
  coord_sf(xlim = c(-76, -65),ylim = c(35, 45)) +
  MyFunctions::my_ggtheme_m()

```

### Density map

This is a simple 2d kenell analysis using `ggplot2::stat_density_2d()` of the sampling points

Looks to me that the stock might not be shifting _per se_ but expanding its northern range

```{r density_map, eval = T, echo = T, fig.width = 12, fig.height = 8}

# Expansion of the stock
ggplot(us_map) +
  geom_sf() +
  geom_sf(data = World_eez_sf, aes(), fill = "white") +
  stat_density_2d(data = map_data, geom = "polygon", 
                  aes(x = lon, y = lat, fill = ..level..)) + 
  geom_point(data = map_data,
             aes(
               x = lon,
               y = lat
             ),
             size = 0.1
  ) +
  facet_wrap(~period,
             nrow = 2) +
  scale_fill_distiller(palette = "Spectral", 
                       guide_legend(title = "Probability \ndensity")) + 
  # viridis::scale_fill_viridis() +
  # viridis::scale_colour_viridis() +
  coord_sf(xlim = c(-76, -65),ylim = c(35, 45)) +
  MyFunctions::my_ggtheme_m()

```

### CPUE (wt CPUE) data

```{r biomass_time_map, eval = T, echo = T, fig.width = 12, fig.height = 8}

ggplot(us_map) +
  geom_sf() +
  geom_sf(data = World_eez_sf, aes(), fill = "white") +
  geom_point(data = subset(map_data, wtcpue < 100),
             aes(
               x = lon,
               y = lat,
               color = wtcpue
             ),
             
  ) +
  # facet_wrap(~period,
  #            nrow = 2) +
  scale_color_distiller(palette = "Spectral", 
                       guide_legend(title = "CPUE")) + 
  # viridis::scale_fill_viridis() +
  # viridis::scale_colour_viridis() +
  coord_sf(xlim = c(-76, -65),ylim = c(35, 45)) +
  MyFunctions::my_ggtheme_m()

```


# ERDDAP

This is the data extracted directly from NOAA in has four available surveys (OceanAdapt only two)

- Northeast Fisheries Science Center (NEFSC) Fall Bottom Trawl Survey	
- Northeast Fisheries Science Center (NEFSC) Spring Bottom Trawl Survey	
- Northeast Fisheries Science Center (NEFSC) Summer Bottom Trawl Survey	
- Northeast Fisheries Science Center (NEFSC) Winter Bottom Trawl Survey	


- Black seabass code is 141
